This invention relates to a system and method for delineating hydrocarbon accumulations. In particular, this invention is drawn to a method and system using a neural network for delineating spatially dependent objects such as hydrocarbon accumulations from seismic data.
1. Field of the Invention
The present invention relates to a system, method, and process for delineating objects in one (1), two (2), or three (3) dimensional space from data that contains patterns related to the existence of said objects. For example, seismic data frequently contains patterns from which hydrocarbon accumulations can be detected through the identification of bright spots, flat spots, and dim spots. In the past, when neural networks have been used for similar purposes other than the detection of hydrocarbon accumulations, it has been necessary to define training sets consisting of data from areas where it is known that certain conditions exist and do not exist. In the case of hydrocarbon accumulations and prior to the disclosures of the present invention, this would have required expensive drilling of oil and gas wells before the data for the training sets could have been acquired. In the method disclosed in the present invention, it is not necessary to use explicitly known training sets to outline the various spatially dependent objects such as hydrocarbon accumulations. By the method disclosed in the present invention, it is possible to automate the interpretation process and quickly provide important information on hydrocarbon accumulations even before drilling commences.
Automated delineation of hydrocarbon accumulations from seismic data will be used as a non-exclusive, actual example to describe the system, method, and process of the present invention. However, the method disclosed is also applicable to a wide range of applications other than hydrocarbon accumulations, such as but not limited to, aeromagnetic profiles, astronomical clusters from radio-telescope data, weather clusters from radiometers, objects from radar, sonar, and infrared returns, etc. Many other applications will be obvious to those skilled in the pertinent art. Accordingly, it is intended by the appended claims to cover all such applications as fall within the true spirit and scope of the present invention.
2. Description of the Prior Art
Many organizations, whether commercial or governmental, have a need to recognize objects from patterns in the data acquired from some sensing process. Spatial delineation of objects is often the first step toward the identification of these objects. Neural networks have been used for this type of delineation and identification in the past. However, prior to the present invention, the neural network approach has generally required that known data be used to form training sets that are used as input to the neural network process. However, acquisition of the known data is often a long and expensive process.
For example, in the oil and gas industry, it is common that seismic data be initially subjected to an interpretation process that is labor intensive. Furthermore, this interpretation is carried out by highly skilled and; therefore, expensive personnel who are limited in the amount of data that they can physically process in a fixed period of time. Even though the interpreters are generally highly skilled and experienced, they are still only able to render subjective judgements as to where hydrocarbon accumulations might exist. Having a clear and accurate areal or spatial delineation of possible hydrocarbon accumulations, i.e. reservoirs, before the interpretation process begins, will greatly improve the accuracy and quality of the interpretation; thereby, reducing the risk in drilling. Drilling of oil and gas wells commonly runs into millions of dollars for each well; and wellbore data, i.e. known data, is not available until this drilling has taken place.
U.S. Pat. No. 5,884,295, which discloses a xe2x80x9cSystem For Neural Network Interpretation of Aeromagnetic Dataxe2x80x9d, is assigned to Texaco, Inc., one of the world""s major oil companies. This patent discloses xe2x80x9ca system for processing Aeromagnetic survey data to determine depth to basement rock;xe2x80x9d and although it does not pertain to the method of the present invention, it is interesting in that it points out xe2x80x9cthe high cost of drilling deep exploratory well holes and collecting reflection seismic data.xe2x80x9d
U.S. Pat. No. 5,444,619 (incorporated herein by reference) is assigned to Schlumberger Technology, a leading seismic processing organization. In this patent, the inventors state that xe2x80x9cSeismic data are routinely and effectively used to estimate the structure of reservoir bodies but often play no role in the essential task of estimating the spatial distribution of reservoir properties. Reservoir property mapping is usually based solely on wellbore data, even when high resolution 3D seismic data are available.xe2x80x9d The Schulumberger patent provides a means for extrapolation of wellbore data throughout a field based on seismic data; however, it does not provide a means for the spatial delineation of reservoir properties, such as the gas cap, permeability zones, porosity zones, etc., prior to the acquisition of wellbore data.
The method of the present invention provides a process of spatially delineating accumulations of various types and properties. For example, it provides an automated process for delineating hydrocarbon accumulations from seismic data. One particular hydrocarbon accumulation is the gas below the cap, i.e. gas cap, in an oil and/or gas field. Being able to accurately delineate the gas cap, from 2D and 3D seismic data, before the interpretation process even begins, will prove to be very valuable to the oil and gas industry. See, for example, U.S. Pat. Nos. 4,279,307, 3,788,398, 4,183,405, and 4,327,805 which all rely on knowledge of the gas cap in their various methods and processes for enhancing hydrocarbon recovery. Accurate delineation of the gas cap, from seismic data, is a long felt and important need in the oil and gas industry.
Numerous U.S. Patents have been issued on the topics of machine vision, image contour recognition, visual recognition, pattern recognition, image edge sensing, object recognition, object tracking, image edge extraction, etc. See, for example, U.S. Pat. Nos. 5,103,488, 5,111,516, 5,313,558, 5,351,309, 5,434,927, 5,459,587, 5,613,039, 5,740,274, 5,754,709, and 5,761,326 that deal with subjects tangentially related to the present invention. Even though the cited patents may in some cases provide superior methods, to that of the present invention, for dealing with each of their particular subjects; these patents indicate the potentially wide range of usage for the novelty included in the present invention and indicate the importance of the disclosure of the present invention. Furthermore, those skilled in the pertinent arts will find a wide range of application for the present invention. It is, therefore, intended by the appended claims to cover all such applications that fall within the true spirit and scope of the present invention. In addition to the patents cited above, a number of specific examples where the present invention might find usage have also been addressed in U.S. Patents.
In U.S. Pat. No. 5,214,744, the inventors describe a method for automatically identifying targets in sonar images where they point out that xe2x80x9cthe noisy nature of sonar images precludes the use of line and edge detection operators.xe2x80x9d Seismic data is also generally recognized as being highly noisy. However, the present invention has been proven to provide a process for accurately delineating hydrocarbon accumulations directly from seismic data. Therefore, it might be expected that, at least in some cases, the present invention might provide another and possibly better process for accomplishing the task described in the sonar patent cited at the start of this paragraph.
U.S. Pat. No. 5,732,697 discloses a xe2x80x9cShift-Invariant Artificial Neural Network for Computerized Detection of Clustered Microcalcifications in Mammography.xe2x80x9d In this disclosure xe2x80x9ca series of digitized medical images are used to train an artificial neural network to differentiate between diseased and normal tissue.xe2x80x9d The present invention might also find application in delineating diseased tissue from the normal or healthy tissue.
U.S. Pat. No. 5,775,806 discloses an Infrared Assessment System for evaluating the xe2x80x9cfunctional status of an object by analyzing its dynamic heat properties using a series of infrared images.xe2x80x9d The present invention might also be used to delineate zones of differing functionality in a series of infrared images.
U.S. Pat. No. 5,776,063, xe2x80x9cAnalysis of Ultrasound Images in the Presence of Contrast Agent,xe2x80x9d describes xe2x80x9can analysis system designed to detect xe2x80x98texturexe2x80x99 characteristics that distinguish healthy tissue from diseased tissue.xe2x80x9d The cited patent also points out that the invention xe2x80x9ccan be applied to characterizing two-dimensional image data derived from X-rays, MRI devices, CT, PET, SPECT, and other image-generating techniques.xe2x80x9d The present invention can also be applied to detecting and delineating texture characteristics that distinguish healthy tissue from diseased tissue.
U.S. Pat. No. 5,777,481, xe2x80x9cIce Detection Using Radiometers,xe2x80x9d discloses an invention that uses xe2x80x9catmospheric radiation as an indicator of atmospheric conditions.xe2x80x9d The present invention can be used to delineate the regions of atmospheric water vapor, cloud water, and ice; and it might be used in conjunction with the cited patent to also identify the content of the regions delineated.
A great deal of recent research has been published relating to the application of artificial neural networks in a variety of contexts. Some examples of this research are presented in the U.S. Patents cited above. Therefore, the purpose of the present invention is not to teach how neural networks might be constructed, but rather to disclose how they can be used to delineate spatially dependent objects from patterns in the data obtained from some sensing process, in particular hydrocarbon accumulations from seismic data, which has been a long standing need prior to the present invention.
While many different types of artificial neural networks exist, two common types are back propagation and radial basis function (RBF) artificial neural networks. Both of these neural network architectures, as well as other architectures, can be used in the method, system, and process disclosed by the present invention. However, the exemplary embodiments used to disclose the method, system, and process of the present invention will be based on the back propagation model.
The system and method disclosed in a co-pending U.S. patent application, Ser. No. 08/974,122, now U.S. Pat. No. 6,119,112, xe2x80x9cOptimum Cessation of Training in Neural Networks,xe2x80x9d which is incorporated herein by reference, is described and utilized in the present invention. However, the system and method disclosed in the co-pending application is merely an expedient used to facilitate the system, method, and process of the present invention. It is not essential to the application of the system, method, and process of the present invention.
It is thus apparent that those of ordinary skill in their various arts will find a wide range of application for the present invention. It is, therefore, intended by the appended claims to cover all such applications as fall within the true spirit and scope of the present invention.
It is also apparent that there has been a long existing need in the art to be able to accurately delineate spatially dependent objects from patterns in the data acquired from some sensing process. The present invention provides such a system, method, and process.
The above-mentioned, long existing needs have been met in accordance with the present invention disclosing a system, method, and process for delineating spatially dependent objects from patterns in the data acquired from some sensing process.
It is therefore one objective of the present invention to disclose how neural networks can be used to delineate spatially dependent objects from patterns in the data acquired from some sensing process.
It is yet another objective of the present invention to disclose how the technique is applied to the automated delineation of hydrocarbon accumulations from seismic data.
It is yet another objective of the present invention to disclose how the appropriate number of nodes and activation function can be determined prior to starting the overall delineation process.
It is yet another objective of the present invention to disclose a system, method, and process for quickly delineating spatially dependent objects, from patterns in the data acquired from some sensing process, when partial knowledge or even intuition as to the approximate delineation is known or can be surmised.
It is yet another objective of the present invention to provide a system, method, and process for detecting the direction in which an object, accumulation, or cluster lies when the sliding window of the present invention is sitting on the edge of the object, accumulation, or cluster.
It is yet another objective of the present invention to provide a system, method, and process for delineating spatially dependent objects, from patterns in the data acquired from some sensing process, when no a priori knowledge or intuition exists as to the delineation.
It is yet another objective of the present invention to provide a system, method, and process for determining whether or not distinguishable object(s) even exist within the data acquired from some sensing process. For example, whether or not it is possible to delineate regions that are characteristic of hydrocarbon reservoirs, within the area covered by a given seismic survey. This objective is accomplished either when a priori knowledge is available, or when no a priori knowledge as to the existence of such delineation, accumulation, reservoir, region, or cluster exists.
It is yet another objective of the present invention to provide a system, method, and process for separating different sub-objects, sub-regions, or sub-clusters that might exist within a given set of data arising out of some sensing process. For example, separating the gas cap from the oil water contact (OWC) in a gas and oil field using seismic data, or separating different porosity, permeability, and productivity zones within a hydrocarbon reservoir. This objective is accomplished even when no a priori knowledge as to the existence of such sub-delineation, sub-accumulation, sub-region, or sub-cluster exists.
It is yet another objective of the present invention to disclose a method for internally validating the correctness of the delineations derived from the system, method, and process of the present invention.
It is yet another objective of the present invention to indicate how the general application of the concepts disclosed in the present invention can be applied to a variety of fields, designs, and physical embodiments and to fit the specific characteristics of different sensory inputs and/or different output requirements.
It is yet another objective of the present invention to indicate that the general concepts disclosed in the present invention can be implemented in parallel on different machines and can be embedded directly in hardware to expedite processing.
Finally, it is yet another objective of the present invention to provide a system, method, and process for predicting future reservoir behavior, i.e. reservoir simulation. This objective is accomplished by combining the methods for detecting and delineating hydrocarbon carbon accumulations, and sub-divisions within the accumulations, directly from seismic data with a priori knowledge related to completion times, production, and pressure properties. Thereby providing a method for reservoir simulation based on the actual parameters present in a particular hydrocarbon accumulation.
In accordance with these and other objectives, the system, method, and process of the present invention are based on the utilization of a neural network to discriminate between differing regions, accumulations, or clusters that can be detected from the patterns present in the data arising out of some sensing process. The neural network classifies particular areas of the data as being either In or Out of a particular region, accumulation, or cluster.
The above as well as additional objects, features, and advantages of the present invention will become apparent in the following detailed written description.
A method is provided for the automated delineation of hydrocarbon accumulations from seismic data gathered in an existing or prospective oil and/or gas field including the steps of developing a neural network using wellbore data indicating productive areas and data indicating nonproductive areas and applying the neural network to at least a portion of the seismic data to distinguish producing areas from non-producing areas of the oil field. The wellbore data indicating productive areas may be gathered from preexisting wells or from wells systematically planned using information provided by the present invention. Also, the data indicating nonproductive areas may be gathered from either an area assumed to be non-productive or from xe2x80x9cdustersxe2x80x9d, i.e. dry wells. The seismic data may be acquired from recording seismic, or any other suitable, data from dynamite, Vibroseis, Thumper, nuclear explosion, earthquake or any other technology or natural event that produces shock waves, or any other type of data which is used to image or display the characteristics of the sub-surface of the earth. The method may also be used to distinguish sub-regions within major accumulations, such as porosity, permeability, high or low productivity zones, etc.
One embodiment of the invention provides a method of delineating hydrocarbon accumulations from seismic data gathered in an oil and/or gas field even when no wells have been drilled, including the steps of developing a neural network within a conceptual sliding window to distinguish accumulations, and applying the neural network to at least a portion of the seismic data to distinguish areas characteristic of hydrocarbon reservoirs from areas without characteristics of hydrocarbon reservoirs. The sliding window may include an xe2x80x9cInxe2x80x9d portion and an xe2x80x9cOutxe2x80x9d portion.
One embodiment of the invention provides a method of delineating mineral accumulations from data relating to a given area including the steps of developing a neural network to distinguish producing areas from non-producing areas of the given area and applying the neural network to at least a portion of the data to distinguish producing areas from non-producing areas. The data may be seismic data, aeromagnetic data, gravity data or any other type of suitable data.
One embodiment of the invention provides a method of delineating spatially dependent characteristics in a given area from data relating to the given area including the steps of developing a neural network to detect and delineate anomalies and applying the neural network to at least a portion of the data to delineate anomalies within the given area. The characteristics may relate to temperature, tissue differences, composition of the material in the area, etc.
One embodiment of the invention provides a method of determining the accuracy of a neural network used for delineating spatially dependent objects from data related to a given area including the steps of developing a first neural network to detect and delineate anomalies in the given area, applying the first neural network to at least a portion of the data to create scores relating to sub-areas of the area, wherein high and low scores indicate the presence or absence of objects within the given area, creating training sets and test sets using data relating to sub-areas which scored high and low relative to the remaining sub-areas, developing a second neural network using the training and test sets to detect and delineate anomalies in the given area, applying the second neural network to at least a portion of the data to create scores relating to sub-areas of the area, and comparing the results of the first, second, third, etc. neural networks to determine the accuracy of a neural network to discriminate on the given data.